Dynamic modification of IO shaping mechanisms of multiple storage nodes in a distributed storage system

ABSTRACT

At least one processing device is configured to detect a failure event impacting at least a first storage node of a plurality of storage nodes of a distributed storage system, and responsive to the detected failure event, to modify an input-output (IO) shaping mechanism in each of the storage nodes in order to at least temporarily reduce a total number of IO operations that are concurrently processed in the distributed storage system. For example, modifying an IO shaping mechanism in each of the storage nodes illustratively comprises transitioning the IO shaping mechanism in each of the storage nodes from a first operating mode to a second operating mode that is different than the first operating mode. The second operating mode of the IO shaping mechanism illustratively has a relatively faster responsiveness to changes in IO operation latency as compared to the first operating mode of the IO shaping mechanism.

FIELD

The field relates generally to information processing systems, and more particularly to storage in information processing systems.

BACKGROUND

A wide variety of different types of distributed storage systems are known. Such storage systems include clustered storage systems as well as other types of storage systems that are distributed across multiple storage nodes. Distributed storage systems can include a potentially large number of distributed storage nodes that are interconnected by a mesh network or other type of communication network. Each such storage node of a distributed storage system typically processes input-output (IO) operations from one or more host devices and in processing those IO operations runs various storage application processes that generally involve interaction of that storage node with one or more other ones of the storage nodes.

SUMMARY

Illustrative embodiments provide techniques for dynamic modification of IO shaping mechanisms of multiple storage nodes in a distributed storage system. For example, in some embodiments, individual IO shaping mechanisms of respective ones of the multiple storage nodes in the distributed storage system are dynamically switched from a first mode using average latency over multiple IO operations to a second mode using latencies of individual IO operations, responsive to a detected failure event. The detected failure event in some embodiments comprises a failure of one or more processes executing on a first storage node, or a failure of one or more storage devices of the first storage node. Additionally or alternatively, the detected failure event in one or more embodiments can comprise a failure of the first storage node itself. Other types of detectable events adversely impacting a desired high availability (HA) of the distributed storage system, also referred to as HA events, are considered to be “failure events” as that term is broadly used herein.

Such embodiments can advantageously prevent a failure event involving a particular one of the storage nodes from unduly interfering with efficient processing of host device IO operations by other ones of the storage nodes of the distributed storage system, thereby significantly improving the overall IO processing performance of the distributed storage system in the presence of a wide variety of different types of storage node failures. For example, host device IO “timeouts” or other substantial IO processing delays that might otherwise arise on the other storage nodes when using conventional approaches are eliminated or substantially reduced in illustrative embodiments disclosed herein.

Illustrative embodiments therefore provide an IO shaping mechanism enhancement to improve distributed storage system stability and provide a higher throughput during unexpected failures.

Using this IO shaping mechanism enhancement, the system will be able to quickly respond to HA events that have an instant and considerable impact on the system, by reducing parallelism before an increased systemic load overwhelms the system. Such embodiments therefore avoid further system degradation that might otherwise occur absent use of the disclosed IO shaping mechanism enhancement.

In one embodiment, an apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to detect a failure event impacting at least a first storage node of a plurality of storage nodes of a distributed storage system, and responsive to the detected failure event, to modify an IO shaping mechanism in each of the storage nodes in order to at least temporarily reduce a total number of IO operations that are concurrently processed in the distributed storage system.

For example, modifying an IO shaping mechanism in each of the storage nodes illustratively comprises transitioning the IO shaping mechanism in each of the storage nodes from a first operating mode to a second operating mode that is different than the first operating mode. The second operating mode of the IO shaping mechanism in some embodiments has a relatively faster responsiveness to changes in IO operation latency as compared to the first operating mode of the IO shaping mechanism.

In some embodiments, the IO shaping mechanism in the first operating mode controls processing of IO operations in order to satisfy one or more constraints relating to average latency over a plurality of IO operations, and the IO shaping mechanism in the second operating mode controls processing of IO operations in order to satisfy one or more constraints relating to latency of individual IO operations.

Modifying operation of an IO shaping mechanism in each of the storage nodes in some embodiments comprises generating a backpressure hint, and sending the backpressure hint to each of the storage nodes, with the IO shaping mechanism being modified in each of the storage nodes responsive to receipt of the backpressure hint.

The generating of the backpressure hint and the sending of the backpressure hint to each of the storage nodes are performed by a system manager of the distributed storage system. For example, sending the backpressure hint to each of the storage nodes illustratively comprises sending the backpressure hint from the system manager of the distributed storage system over a dedicated channel to each of the storage nodes. A given one of the storage nodes in response to receipt of the backpressure hint modifies its IO shaping mechanism to effectively reduce a number of parallel IO operations processed by the given storage node.

The IO shaping mechanism in a given one of the storage nodes is illustratively configured to measure IO operation latency and to adjust a number of parallel IO operations processed by the given storage node responsive to the measurement of IO operation latency.

In some embodiments, modifying an IO shaping mechanism in each of the storage nodes comprises identifying a type of the detected failure event, and providing a particular one of a plurality of available modifications of the IO shaping mechanism in a given one of the storage nodes based at least in part on the identified type of the detected failure event. For example, different ones of the plurality of available modifications of the IO shaping mechanism illustratively provide different levels or other types of reductions in number of parallel IO operations processed by the given storage node.

The storage nodes of the distributed storage system are illustratively interconnected in a mesh network, although other interconnection arrangements may be used. The distributed storage system includes persistent storage illustratively comprising a first plurality of storage devices associated with the first storage node and one or more additional pluralities of storage devices associated with respective additional ones of the storage nodes. The storage devices associated with a given one of the storage nodes are illustratively implemented in a disk array enclosure or other type of storage array enclosure of the given storage node.

Each of at least a subset of the storage nodes of the distributed storage system in some embodiments illustratively comprises a set of processing modules configured to communicate with corresponding sets of processing modules on other ones of the storage nodes, with a given such set of processing modules comprising, for example, at least a routing module, a control module and a data module. The sets of processing modules of the respective storage nodes of the distributed storage system collectively comprise at least a portion of a distributed storage controller of the distributed storage system.

These and other illustrative embodiments include, without limitation, apparatus, systems, methods and processor-readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an information processing system comprising a distributed storage system incorporating functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in an illustrative embodiment.

FIG. 2 shows an example of a distributed content addressable storage (CAS) system that illustratively represents one possible implementation of a distributed storage system in some embodiments.

FIG. 3 shows an example relationship between routing, control and data modules of the distributed CAS system of FIG. 2 in an illustrative embodiment.

FIG. 4 is a flow diagram of an example process for dynamic modification of IO shaping mechanisms of multiple storage nodes in an illustrative embodiment.

FIGS. 5 and 6 show examples of processing platforms that may be utilized to implement at least a portion of an information processing system in illustrative embodiments.

DETAILED DESCRIPTION

Illustrative embodiments will be described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that these and other embodiments are not restricted to the particular illustrative system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, processing systems comprising cloud computing and storage systems, as well as other types of processing systems comprising various combinations of physical and virtual processing resources. An information processing system may therefore comprise, for example, at least one data center or other cloud-based system that includes one or more clouds hosting multiple tenants that share cloud resources. Numerous different types of enterprise computing and storage systems are also encompassed by the term “information processing system” as that term is broadly used herein.

FIG. 1 shows an information processing system 100 configured in accordance with an illustrative embodiment. The information processing system 100 comprises a plurality of host devices 101, a first storage node 102-1, a second storage node 102-2, and a plurality of additional storage nodes 102-3 through 102-N, all of which are configured to communicate with one another over a network 104. The first and second storage nodes 102 with the additional storage nodes 102-3 through 102-N collectively form an example of what is more generally referred to herein as a “distributed storage system” or DSS. Other distributed storage systems can include different numbers and arrangements of storage nodes. For example, a distributed storage system in some embodiments may include only the first and second storage nodes 102.

Each of the storage nodes 102 is illustratively configured to interact with one or more of the host devices 101. The host devices 101 illustratively comprise servers or other types of computers of an enterprise computer system, cloud-based computer system or other arrangement of multiple compute nodes associated with respective users.

The host devices 101 in some embodiments illustratively provide compute services such as execution of one or more applications on behalf of each of one or more users associated with respective ones of the host devices 101. Such applications illustratively generate input-output (IO) operations that are processed by a corresponding one of the storage nodes 102. The term “input-output” as used herein refers to at least one of input and output. For example, IO operations may comprise write requests and/or read requests directed to logical addresses of a particular logical storage volume of a given one of the storage nodes 102. These and other types of IO operations are also generally referred to herein as IO requests.

The storage nodes 102 illustratively comprise respective processing devices of one or more processing platforms. For example, the storage nodes 102 can each comprise one or more processing devices each having a processor and a memory, possibly implementing virtual machines and/or containers, although numerous other configurations are possible.

The storage nodes 102 can additionally or alternatively be part of cloud infrastructure such as an Amazon Web Services (AWS) system. Other examples of cloud-based systems that can be used to provide at least portions of the storage nodes 102 include Google Cloud Platform (GCP) and Microsoft Azure.

The storage nodes 102 may be implemented on a common processing platform, or on separate processing platforms.

The host devices 101 are illustratively configured to write data to and read data from the distributed storage system comprising storage nodes 102 in accordance with applications executing on those host devices for system users.

The term “user” herein is intended to be broadly construed so as to encompass numerous arrangements of human, hardware, software or firmware entities, as well as combinations of such entities. Compute and/or storage services may be provided for users under a Platform-as-a-Service (PaaS) model, an Infrastructure-as-a-Service (IaaS) model and/or a Function-as-a-Service (FaaS) model, although it is to be appreciated that numerous other cloud infrastructure arrangements could be used. Also, illustrative embodiments can be implemented outside of the cloud infrastructure context, as in the case of a stand-alone computing and storage system implemented within a given enterprise.

The network 104 is assumed to comprise a portion of a global computer network such as the Internet, although other types of networks can be part of the network 104, including a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks. The network 104 in some embodiments therefore comprises combinations of multiple different types of networks each comprising processing devices configured to communicate using Internet Protocol (IP) or other communication protocols.

As a more particular example, some embodiments may utilize one or more high-speed local networks in which associated processing devices communicate with one another utilizing Peripheral Component Interconnect express (PCIe) cards of those devices, and networking protocols such as InfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternative networking arrangements are possible in a given embodiment, as will be appreciated by those skilled in the art.

The first storage node 102-1 comprises a plurality of storage devices 106-1 and an associated storage controller 108-1. The storage devices 106-1 store metadata pages 120-1 and user data pages 122-1 associated with one or more storage volumes of the distributed storage system. The storage volumes illustratively comprise respective logical units (LUNs) or other types of logical storage volumes. The storage devices 106-1 more particularly comprise local persistent storage devices of the first storage node 102-1. Such persistent storage devices are local to the first storage node 102-1, but remote from the second storage node 102-2 and the other storage nodes 102.

Similarly, the second storage node 102-2 comprises a plurality of storage devices 106-2 and an associated storage controller 108-2. The storage devices 106-2 store metadata pages 120-2 and user data pages 122-2 associated with one or more storage volumes of the distributed storage system, such as the above-noted LUNs. The storage devices 106-2 more particularly comprise local persistent storage devices of the second storage node 102-2. Such persistent storage devices are local to the second storage node 102-2, but remote from the first storage node 102-1 and the other storage nodes 102.

The storage controller 108-1 of first storage node 102-1 in the FIG. 1 embodiment includes local and remote interface logic 110-1 and an IO shaping mechanism 111-1. It can also include additional elements, such as journal destaging logic and other logic instances for processing IO operations, and a signature generator for generating content-based signatures of respective data pages.

Similarly, the storage controller 108-2 of second storage node 102-2 includes local and remote interface logic 110-2 and an IO shaping mechanism 111-2. The storage controller 108-2, like the storage controller 108-1, can also include additional elements, such as journal destaging logic and other logic instances for processing IO operations, and a signature generator for generating content-based signatures of respective data pages.

The instances of local and remote interface logic 110-1 and 110-2 are collectively referred to herein as local and remote interface logic 110. Such local and remote interface logic instances are also referred to herein as individually or collectively comprising distributed logic instances of the system 100.

The local and remote interface logic 110 of the storage nodes 102 controls interaction of the storage nodes 102 with local and remote storage devices 106 of the distributed storage system. The local persistent storage of a given one of the storage nodes 102 illustratively comprises the particular local persistent storage devices that are implemented in or otherwise associated with that storage node. It is assumed that such local persistent storage devices of the given storage node are accessible to the storage controller of that node via a local interface, and are accessible to storage controllers 108 of respective other ones of the storage nodes 102 via remote interfaces. The local and remote interface logic 110 illustratively controls the manner in which the local and remote interfaces are used to access persistent storage of the given node, as will be described in more detail elsewhere herein.

For example, it is assumed in some embodiments each of the storage devices 106 on a given one of the storage nodes 102 can be accessed by the given storage node via its local interface, or by any of the other storage nodes via a remote direct memory access (RDMA) interface. A given storage application executing on the storage nodes 102 illustratively requires that all of the storage nodes 102 be able to access all of the storage devices 106. Such access to local persistent storage of each node from the other storage nodes can be performed, for example, using the RDMA interfaces with the other storage nodes, although other arrangements are possible.

The storage controllers 108 are illustratively configured to control performance of at least a portion of a process as described herein for dynamic modification of IO shaping mechanisms 111 of storage nodes 102. For example, the storage controllers 108 and their respective IO shaping mechanisms 111 illustratively implement at least portions of the process to be described below in conjunction with FIG. 4. This process an example of an algorithm implemented by logic instances deployed within the storage controllers 108.

Each of the other storage nodes 102 of the distributed storage system of FIG. 1 is assumed to be configured in a manner similar to that described above for the first storage node 102-1 and the second storage node 102-2.

The storage controllers 108 of the storage nodes 102 may include additional modules and other components typically found in conventional implementations of storage controllers and storage systems, although such additional modules and other components are omitted from the figure for clarity and simplicity of illustration.

For example, the storage controllers 108 can comprise or be otherwise associated with a write cache and a write cache journal, both also illustratively distributed across the storage nodes 102 of the distributed storage system. It is further assumed in illustrative embodiments that one or more additional journals are provided, including at least a metadata update journal. Illustrative embodiments disclosed herein may be configured to perform destaging of a metadata update journal in conjunction with metadata recovery responsive to a failure event impacting one or more of the storage nodes 102, and to perform additional or alternative functions in conjunction with processing of IO operations.

The storage devices 106 of the storage nodes 102 illustratively comprise solid state drives (SSDs). Such SSDs are implemented using non-volatile memory (NVM) devices such as flash memory. Other types of NVM devices that can be used to implement at least a portion of the storage devices 106 include non-volatile random access memory (NVRAM), phase-change RAM (PC-RAM), magnetic RAM (MRAM), resistive RAM, spin torque transfer magneto-resistive RAM (STT-MRAM), and Intel Optane™ devices based on 3D XPoint™ memory. These and various combinations of multiple different types of NVM devices may also be used. For example, hard disk drives (HDDs) can be used in combination with or in place of SSDs or other types of NVM devices.

However, it is to be appreciated that other types of storage devices can be used in other embodiments. For example, a given storage system as the term is broadly used herein can include a combination of different types of storage devices, as in the case of a multi-tier storage system comprising a flash-based fast tier and a disk-based capacity tier. In such an embodiment, each of the fast tier and the capacity tier of the multi-tier storage system comprises a plurality of storage devices with different types of storage devices being used in different ones of the storage tiers. For example, the fast tier may comprise flash drives while the capacity tier comprises HDDs. The particular storage devices used in a given storage tier may be varied in other embodiments, and multiple distinct storage device types may be used within a single storage tier. The term “storage device” as used herein is intended to be broadly construed, so as to encompass, for example, SSDs, HDDs, flash drives, hybrid drives or other types of storage devices. Such storage devices are examples of local persistent storage devices 106 of the storage nodes 102 of the distributed storage system of FIG. 1.

In some embodiments, the storage nodes 102 of the distributed storage system collectively provide a scale-out all-flash content addressable storage array such as an XtremIO™ storage array from Dell EMC of Hopkinton, Mass. The storage nodes 102 can be used to implement other types of storage systems in other embodiments. One or more such storage nodes can be associated with at least one storage array, including by way of example one or more VNX®, VMAX®, Unity™ or PowerMax™ storage arrays, commercially available from Dell EMC. Additional or alternative types of storage products that can be used in implementing a given storage system in illustrative embodiments include software-defined storage, cloud storage, object-based storage and scale-out storage. Combinations of multiple ones of these and other storage types can also be used.

The term “storage system” as used herein is therefore intended to be broadly construed, and should not be viewed as being limited to content addressable storage systems or flash-based storage systems. A given storage system as the term is broadly used herein can comprise, for example, network-attached storage (NAS), storage area networks (SANs), direct-attached storage (DAS) and distributed DAS, as well as combinations of these and other storage types, including software-defined storage.

In some embodiments, communications between the host devices 101 and the storage nodes 102 comprise Small Computer System Interface (SCSI) or Internet SCSI (iSCSI) commands. Other types of SCSI or non-SCSI commands may be used in other embodiments, including commands that are part of a standard command set, or custom commands such as a “vendor unique command” or VU command that is not part of a standard command set. The term “command” as used herein is therefore intended to be broadly construed, so as to encompass, for example, a composite command that comprises a combination of multiple individual commands. Numerous other commands can be used in other embodiments.

For example, although in some embodiments certain commands used by the host devices 101 to communicate with the storage nodes 102 illustratively comprise SCSI or iSCSI commands, other embodiments can implement IO operations utilizing command features and functionality associated with NVM Express (NVMe), as described in the NVMe Specification, Revision 1.3, May 2017, which is incorporated by reference herein. Other storage protocols of this type that may be utilized in illustrative embodiments disclosed herein include NVMe over Fabric, also referred to as NVMeoF, and NVMe over Transmission Control Protocol (TCP), also referred to as NVMe/TCP.

The distributed storage system of FIG. 1 implements functionality for dynamic modification of IO shaping mechanisms 111 of the storage nodes 102, utilizing instances of local and remote interface logic 110 of the storage controllers 108 of respective ones of the storage nodes 102, as will now be described in more detail.

As indicated previously, the storage nodes 102 of the distributed storage system of FIG. 1 process IO operations from one or more host devices 101 and in processing those IO operations run various storage application processes that generally involve interaction of that storage node with one or more other ones of the storage nodes.

Absent use of the techniques disclosed herein, failure events involving one or more of the storage nodes 102 can unduly impact the processing efficiency of other ones of the storage nodes 102, leading to potentially severe degradations in the overall IO processing performance of the distributed storage system.

The distributed storage system of FIG. 1 and other illustrative embodiments herein advantageously overcome these and other problems arising from failure events by providing dynamic modification of IO shaping mechanisms over multiple storage nodes. For example, the distributed storage system of FIG. 1 is illustratively configured to provide dynamic modification of IO shaping mechanisms 111 of respective ones of the storage nodes 102. In some embodiments, this is achieved by detecting a failure event impacting at least one storage node of the storage nodes 102 of the distributed storage system, and responsive to the detected failure event, modifying the IO shaping mechanisms 111 in respective ones of the storage nodes 102 in order to at least temporarily reduce a total number of IO operations that are concurrently processed in the distributed storage system. The IO operations that are being concurrently processed in the system in some embodiments are referred to herein as “in-flight” IOs that have been admitted by the storage nodes 102 to further processing within the system. The storage nodes 102 are illustratively configured to queue IO operations arriving from one or more of the host devices 101 in one or more sets of IO queues until such IOs are admitted into the system for further processing at least in part under the control of the IO shaping mechanisms 111.

It is assumed in some embodiments that each of the storage nodes 102 incorporates substantially the same functionality for dynamic modification of its corresponding one of the IO shaping mechanisms 111.

The detected failure event in some embodiments comprises a failure of one or more processes executing on at least one of the storage nodes 102 and/or a failure of one or more of the local persistent storage devices 106 of at least one of the storage nodes 102. Additionally or alternatively, the detected failure event in one or more embodiments can comprise a failure of at least one of the storage nodes 102. Other types of detectable events adversely impacting a desired high availability (HA) of the distributed storage system, also referred to as HA events, are considered to be “failure events” as that term is broadly used herein.

In some embodiments, the storage nodes 102 of the distributed storage system of FIG. 1 are connected to each other in a full mesh network, and are collectively managed by a system manager. A given set of local persistent storage devices 106 on a given one of the storage nodes 102 is illustratively implemented in a disk array enclosure (DAE) or other type of storage array enclosure of that storage node. The system manager is illustratively implemented as a management module or other similar management logic instance, possibly running on one or more of the storage nodes 102, on another storage node and/or on a separate non-storage node of the distributed storage system.

In some embodiments, modifying an IO shaping mechanism in each of the storage nodes 102 comprises transitioning the IO shaping mechanism in each of the storage nodes 102 from a first operating mode to a second operating mode that is different than the first operating mode. For example, the second operating mode of the IO shaping mechanism illustratively has a relatively faster responsiveness to changes in IO operation latency as compared to the first operating mode of the IO shaping mechanism.

As a more particular example, the IO shaping mechanism in the first operating mode illustratively controls processing of IO operations in order to satisfy one or more constraints relating to average latency over a plurality of IO operations, and the IO shaping mechanism in the second operating mode illustratively controls processing of IO operations in order to satisfy one or more constraints relating to latency of individual IO operations.

In these and other arrangements, the second operating mode provides enhanced responsiveness to adverse impacts of a failure event on individual IO operations relative to the first operating mode.

In some embodiments, modifying operation of an IO shaping mechanism in each of the storage nodes 102 illustratively comprises generating a backpressure hint, and sending the backpressure hint to each of the storage nodes 102. The IO shaping mechanism is modified in each of the storage nodes 102 responsive to receipt of the backpressure hint. The generation of the backpressure hint and the sending of the backpressure hint to each of the storage nodes 102 are performed by the above-noted system manager of the distributed storage system, but can additionally or alternatively involve other system components in other embodiments. The sending of the backpressure hint to each of the storage nodes illustratively comprises sending the backpressure hint from the system manager of the distributed storage system over a dedicated channel to each of the storage nodes 102. Any of a wide variety of different communication channel types can be used to provide such a dedicated channel between a system manager and each of the storage nodes, although it is to be appreciated that use of a dedicated channel is not a requirement.

A given one of the storage nodes 102 in response to receipt of the backpressure hint modifies its IO shaping mechanism to effectively reduce a number of parallel IO operations processed by the given storage node.

The backpressure hint can take any of a number of different forms, from an indication of the detected failure event and possibly the particular type of detected failure event within a range of detectable failure events, to a reduction goal indicating particular adjustments that should be made by the corresponding IO shaping mechanism to limit a number of in-flight IOs being concurrently processed by the system. The term “backpressure hint” as used herein is therefore intended to be broadly construed.

The IO shaping mechanism in a given one of the storage nodes 102 is illustratively configured to measure IO operation latency and to adjust a number of parallel IO operations processed by the given storage node responsive to the measurement of IO operation latency.

In some embodiments, modifying an IO shaping mechanism in each of the storage nodes 102 comprises identifying a type of the detected failure event, and providing a particular one of a plurality of available modifications of the IO shaping mechanism in a given one of the storage nodes 102 based at least in part on the identified type of the detected failure event. Different ones of the plurality of available modifications of the IO shaping mechanism illustratively provide different reductions in number of parallel IO operations processed by the given storage node.

The IO shaping mechanisms 111 in some embodiments are implemented as respective “choker” mechanisms that are used to adjust the number of concurrent IOs in the distributed storage system. A given such choker mechanism illustratively operates by measuring latency of host IOs and reducing or increasing the number of parallel IOs in the system accordingly, in order to avoid long latency and host IO timeouts. In its normal mode of operation, the choker mechanism is configured to increase or decrease the number of IOs to the system relatively slowly, to ensure a smooth performance experience for the host devices during the normal mode. The choker mechanism is therefore configured to adjust the number of concurrent IOs in the system in order to achieve a desired host IO latency over multiple IOs generated by that host, possibly using an average host IO latency over the multiple IOs.

However, HA events can cause an instantly high load on the system which may eventually lead to host IO timeouts. Such HA events, also referred to as “failure events” herein, include a wide variety of different types of failures to system components, such as node failure, journal failure, single or double SSD failures, etc. During such HA events, various recovery flows (e.g., node failover, journal recovery, RAID recovery, etc.) cause an increased load on the system, such as more IOs to the storage devices, more network utilization and more CPU calculations required for the recovery procedures.

The choker mechanism is illustratively configured to ensure that excessive traffic cannot be ingested by the system beyond its processing capacity. The choker mechanism assumes that the system bottlenecks are known and are correctly reflected by the IO latencies or other similar performance measures.

However, in failure event scenarios the system bottlenecks can change drastically, with typical non-bottleneck resources becoming bottlenecks. For example, one or more storage nodes might stop responding for all IO processing requests.

In a possible scenario of this type, assume that one storage node out of 16 storage nodes stops responding to requests. Assuming a uniform spread of such requests across the storage nodes, only one out of every 16 requests is impacted by the bottleneck, while others are serviced. That results in a very slight decrease of latency for the serviced requests, although with a rapid buildup of requests on the bottlenecked resources. Such a condition often quickly escalates, resulting in internal queue overflows and new error conditions which can cascade to produce a total system breakdown.

For example, SSD timeouts may lead to journal destaging failures, and journal destaging failures may eventually cause the upstream IO to fail. Also, inter-node communication messages may timeout, which may lead to processing module restarts, and further recovery flows associated with those restarts.

Illustrative embodiments herein address these and other problematic situations by providing an IO shaping mechanism enhancement. The IO shaping mechanism enhancement in some embodiments utilizes a system manager to detect HA events and to control modification of the choker mechanisms, using the following algorithm:

1. The system manager monitors for HA events and identifies when such an HA event has occurred.

2. Responsive to the occurrence of an HA event, the system manager will send a backpressure hint to each storage node, through a dedicated channel, to indicate that such an event has occurred.

3. The choker mechanisms will respond to these backpressure hints by drastically lowering the parallel IOs allowed to enter the system, instead of waiting for the “natural” reduction of parallelism in the normal operating mode of the choker mechanism which would be too slow to respond to such abrupt changes in system capabilities.

4. In this enhanced responsiveness mode, the choker mechanism is configured to respond to any late IO and not on average latency, as in normal mode. The enhanced responsiveness mode prevents any internal IO buildup on some unknown bottleneck.

5. After the HA event has been handled by the system manager, the choker mechanism will resume its operation to normal mode, ensuring maximum throughput. In this mode, the system bottlenecks are well characterized and the average latencies correctly reflect the IO capacity of the system.

In some embodiments, as different HA events or other failure events result in different recovery flows within the system, different types of reductions of parallelism can be applied by the modified IO shaping mechanisms based on the particular type of failure event that is detected by the system manager.

The above-described choker mechanism in some embodiments is operative to limit the number of in-flight IOs being processed within the system (by adjustment of what is referred to herein as the “choker size”), according to the average latency. The number of in-flight IOs is an example of what is more generally referred to herein as the number of IOs being “concurrently processed” within the system.

For example, when the average latency is low, the choker mechanism allows more incoming IOs to enter the system for further processing, and when the average latency is high, it prevents incoming IOs from entering the system for further processing.

In the normal mode of the choker mechanism, the choker size changes in relatively small steps, such as one step after some number of latency monitoring cycles. The step size may be a tunable parameter of the system.

In the enhanced responsiveness mode, entered after detection of an HA event, the choker size is illustratively lowered by multiple steps immediately. Accordingly, the choker size is lowered without waiting for multiple latency monitoring cycles, as such waiting would otherwise take a relatively long time while allowing additional IOs to enter the system for further processing and thereby causing further problems.

In addition, in the enhanced responsiveness mode, the choker mechanism is illustratively configured to respond (e.g., to update the choker size) more quickly (e.g., after one or very few monitoring cycles as compared to more monitoring cycles in normal mode) and will respond to individual IO latencies (e.g., the longest latencies, also referred to as “tail” latencies) in addition to or in place of the average latency.

The above-described choker mechanism features are examples only, and should not be construed as limiting in any way. Other types of IO shaping mechanisms may be used in other embodiments, and the term “IO shaping mechanism” as used herein is intended to be broadly construed. For example, an IO shaping mechanism implemented in a given storage node of a distributed storage system is illustratively configured to control at least a portion of a total amount of in-flight IOs being processed within the system, by controlling the manner in which IOs received from one or more host devices are permitted to enter further processing in the system via the given storage node. Such an IO shaping mechanism in some embodiments is configured to interact with one or more sets of IO queues of the given storage node in order to queue IOs received from the hosts until such time as those IOs are admitted for further processing under the control of the IO shaping mechanism. Other types of IO shaping mechanisms exhibiting different features and functionality can also be used.

These and other embodiments disclosed herein advantageously provide an IO shaping mechanism enhancement to improve distributed storage system stability and provide a higher throughput during unexpected failures.

Using this IO shaping mechanism enhancement, the system will be able to quickly respond to HA events that have an instant and considerable impact on the system, by reducing parallelism before an increased systemic load overwhelms the system. Such embodiments therefore avoid further system degradation that might otherwise occur absent use of the disclosed IO shaping mechanism enhancement.

The above-described functionality associated with dynamic modification of IO shaping mechanisms 111 in the distributed storage system of FIG. 1 is illustratively implemented at least in part by or under the control of the instances of local and remote interface logic 110 operating in cooperation with the IO shaping mechanisms in the storage controllers 108 of the respective first and second storage nodes 102. The other storage nodes 102 of the distributed storage system of FIG. 1 are assumed to operate in a manner similar to that described above for the first and second storage nodes 102.

Such functionality provides illustrative examples of processes for dynamic modification of IO shaping mechanisms of multiple storage nodes in a distributed storage system. Additional or alternative steps may be used in such a process in other embodiments. Also, the ordering of the steps can be varied, and different portions of the process can be performed at least in part in parallel with one another.

An additional illustration of an example process for dynamic modification of IO shaping mechanisms implementing at least some of the above-described functionality will be provided below in conjunction with the flow diagram of FIG. 4.

The storage nodes 102 in some embodiments each implement substantially the same functionality for dynamic modification of IO shaping mechanisms via their respective instances of local and remote interface logic 110.

The storage nodes 102 collectively comprise an example of a distributed storage system. The term “distributed storage system” as used herein is intended to be broadly construed, so as to encompass, for example, clustered storage systems or other types of storage systems distributed over multiple storage nodes. A given such storage node can comprise a set of processing modules configured to communicate with corresponding sets of processing modules on other ones of the storage nodes. The sets of processing modules of the storage nodes collectively comprise a distributed storage controller of the distributed storage system.

The storage nodes 102 in some embodiments are part of a distributed content addressable storage system in which logical addresses of data pages are mapped to physical addresses of the data pages in the storage devices 106 using respective content-based signatures that are generated from those data pages, as will now be described in more detail with reference to the illustrative embodiments of FIGS. 2 and 3.

FIG. 2 shows an example of a distributed content addressable storage (CAS) system 205 that illustratively represents a particular example implementation of the distributed storage system comprising the storage nodes 102 of FIG. 1. The distributed CAS system 205 is therefore assumed to be coupled to one or more host devices 101 of a computer system within information processing system 100.

The CAS system 205 comprises a plurality of persistent storage devices 206 and an associated storage controller 208. The storage devices 206 store data of a plurality of storage volumes. The storage volumes illustratively comprise respective LUNs or other types of logical storage volumes. The stored data comprises metadata pages 220 and user data pages 222, both described in more detail elsewhere herein. The storage devices 206 and storage controller 208 are distributed across multiple storage nodes 202. The CAS system 205 can include additional components, such as local and remote interface logic and journal destaging logic, each also illustratively distributed across the storage nodes 202 of the CAS system 205.

The CAS system 205 is illustratively implemented as a distributed storage system, also referred to herein as a clustered storage system, in which each of at least a subset of the storage nodes 202 comprises a set of processing modules configured to communicate with corresponding sets of processing modules on other ones of the storage nodes 202. The sets of processing modules of the storage nodes of the CAS system 205 collectively comprise at least a portion of the storage controller 208 of the CAS system 205. For example, in some embodiments the sets of processing modules of the storage nodes collectively comprise a distributed storage controller of the CAS system 205. A “distributed storage system” as that term is broadly used herein is intended to encompass any storage system that, like the CAS system 205, is distributed across multiple storage nodes.

Although it is assumed that both the first storage node 102-1 and the second storage node 102-2 are part of a single content addressable storage system in some embodiments, other types of storage systems can be used for one or both of the first storage node 102-1 and the second storage node 102-2 in other embodiments. For example, it is possible that at least one of the storage nodes 102 in an illustrative embodiment need not be a storage node of a content addressable storage system and such a storage node need not include an ability to generate content-based signatures. In an embodiment of this type, the signature generation functionality can be implemented in a host device.

The storage controller 208 in the present embodiment is configured to implement functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes of the type previously described in conjunction with FIG. 1.

The storage controller 208 includes a distributed write cache 212 and a set of distributed journals 214. The set of distributed journals 214 illustratively comprises a write cache journal, a metadata update journal and possibly one or more other journals. The distributed write cache 212 and the set of distributed journals 214 each illustratively have respective instances thereof on each of the storage nodes 202.

Additional modules that are assumed to be implemented in the storage controller 208 but are not explicitly shown in the figure include, for example, distributed instances of local and remote interface logic 110 and IO shaping mechanisms 111, with respective different instances thereof being implemented on respective ones of the storage nodes 202. Each of the storage nodes 202 of the CAS system 205 is assumed to be implemented using at least one processing device comprising a processor coupled to a memory.

In the CAS system 205, logical addresses of data pages are mapped to physical addresses of the data pages using respective content-based signatures that are generated from those data pages. The data pages illustratively include user data pages 222. Metadata pages 220 are typically handled in a different manner, as will be described.

The term “page” as used in this and other contexts herein is intended to be broadly construed so as to encompass any of a wide variety of different types of blocks that may be utilized in a block storage device of a storage system. Different native page sizes are generally utilized in different storage systems of different types. For example, XtremIO™ X1 storage arrays utilize a native page size of 8 kilobytes (KB), while XtremIO™ X2 storage arrays utilize a native page size of 16 KB. Larger native page sizes of 64 KB and 128 KB are utilized in VMAX® V2 and VMAX® V3 storage arrays, respectively. The native page size generally refers to a typical page size at which the storage system ordinarily operates, although it is possible that some storage systems may support multiple distinct page sizes as a configurable parameter of the system. Each such page size of a given storage system may be considered a “native page size” of the storage system as that term is broadly used herein.

A given “page” as the term is broadly used herein should therefore not be viewed as being limited to any particular range of fixed sizes. In some embodiments, a page size of 8 KB is used, but this is by way of example only and can be varied in other embodiments. For example, page sizes of 4 KB, 16 KB or other values can be used. Accordingly, illustrative embodiments can utilize any of a wide variety of alternative paging arrangements for organizing data pages of the CAS system 205.

Also, the term “storage volume” as used herein is intended to be broadly construed, and should not be viewed as being limited to any particular format or configuration.

The content-based signatures utilized in some embodiments illustratively comprise respective hash digests of respective data pages of a storage volume. A given one of the hash digests is generated in illustrative embodiments by applying a secure hashing algorithm to content of a corresponding one of the data pages of the storage volume. For example, a given hash digest can be generated by application of a hash function such as the well-known Secure Hashing Algorithm 1 (SHA1) to the content of its corresponding data page. Other types of secure hashing algorithms, such as SHA2 or SHA256, or more generally other hash functions, can be used in generating content-based signatures herein.

A given hash digest in illustrative embodiments is unique to the particular content of the page from which it is generated, such that two pages with exactly the same content will have the same hash digest, while two pages with different content will have different hash digests. It is also possible that other types of content-based signatures may be used, such as hash handles of the type described elsewhere herein. A hash handle generally provides a shortened representation of its corresponding hash digest. More particularly, the hash handles are shorter in length than respective hash digests that are generated by applying a secure hashing algorithm to respective ones of the data pages. Hash handles are considered examples of “content-based signatures” as that term is broadly used herein.

As indicated above, the storage controller 208 in this embodiment is implemented as a distributed storage controller that comprises sets of processing modules distributed over the storage nodes 202. The storage controller 208 is therefore an example of what is more generally referred to herein as a distributed storage controller.

It is assumed in some embodiments that the processing modules of the storage controller 208 are interconnected in a full mesh network, such that a process of one of the processing modules can communicate with processes of any of the other processing modules. Commands issued by the processes can include, for example, remote procedure calls (RPCs) directed to other ones of the processes.

The sets of processing modules of the storage controller 208 illustratively comprise control modules 208C, data modules 208D, routing modules 208R and at least one management module 208M. Again, these and possibly other processing modules of the storage controller 208 are illustratively interconnected with one another in the full mesh network, such that each of the modules can communicate with each of the other modules, although other types of networks and different module interconnection arrangements can be used in other embodiments.

The management module 208M of the distributed storage controller in this embodiment may more particularly comprise a system-wide management module, also referred to herein as a system manager. Other embodiments can include multiple instances of the management module 208M implemented on different ones of the storage nodes 202. It is therefore assumed that the storage controller 208 comprises one or more management modules 208M.

A wide variety of alternative configurations of nodes and processing modules are possible in other embodiments. Also, the term “storage node” as used herein is intended to be broadly construed, and may comprise a node that implements storage control functionality but does not necessarily incorporate storage devices.

The processing modules of the storage controller 208 as disclosed herein utilize metadata structures that include logical layer and physical layer mapping tables to be described below. It is to be appreciated that these particular tables are only examples, and other tables or metadata structures having different configurations of entries and fields can be used in other embodiments. The logical layer and physical layer mapping tables in this embodiment illustratively include the following:

1. An address-to-hash (“A2H”) table. The A2H table illustratively comprises a plurality of entries accessible utilizing logical addresses as respective keys, with each such entry of the A2H table comprising a corresponding one of the logical addresses, a corresponding one of the hash handles, and possibly one or more additional fields. In some embodiments, the A2H table is assumed to comprise full hash digests in place of or in addition to hash handles. Other configurations are possible, and the term “address-to-hash table” as used herein is therefore intended to be broadly construed.

2. A hash-to-data (“H2D”) table. The H2D table illustratively comprises a plurality of entries accessible utilizing hash handles as respective keys, with each such entry of the H2D table comprising a corresponding one of the hash handles, a physical offset of a corresponding one of the data pages, and possibly one or more additional fields. Again, full hash digests can be used in place of or in addition to hash handles.

3. A hash metadata (“HMD”) table. The HMD table illustratively comprises a plurality of entries accessible utilizing hash handles as respective keys. Each such entry of the HMD table comprises a corresponding one of the hash handles, a corresponding reference count and a corresponding physical offset of one of the data pages. A given one of the reference counts denotes the number of logical pages in the storage system that have the same content as the corresponding data page and therefore point to that same data page via their common hash digest. The HMD table illustratively comprises at least a portion of the same information that is found in the H2D table. Accordingly, in other embodiments, those two tables can be combined into a single table, illustratively referred to as an H2D table, an HMD table or another type of physical layer mapping table providing a mapping between hash values, such as hash handles or hash digests, and corresponding physical addresses of data pages.

4. A physical layer based (“PLB”) table. The PLB table illustratively comprises a plurality of entries accessible utilizing physical offsets as respective keys, with each such entry of the PLB table comprising a corresponding one of the physical offsets, a corresponding one of the hash digests, and possibly one or more additional fields.

As indicated above, the hash handles are generally shorter in length than the corresponding hash digests of the respective data pages, and each illustratively provides a short representation of the corresponding full hash digest. For example, in some embodiments, the full hash digests are 20 bytes in length, and their respective corresponding hash handles are illustratively only 4 or 6 bytes in length. Hash digests can be used in place of or in addition to hash handles in some embodiments.

Again, the logical layer and physical layer mapping tables referred to above are examples only, and can be varied in other embodiments. For example, other types of hash-to-physical (“H2P”) mapping tables may be used in addition to or in place of the above-noted H2D, HMD and/or PLB tables.

In some embodiments, certain ones of the above-described mapping tables are maintained by particular modules of storage controller 208. For example, the mapping tables maintained by the control modules 208C illustratively comprise at least one A2H table and possibly also at least one H2D table. The A2H tables are utilized to store address-to-hash mapping information and the H2D tables are utilized to store hash-to-data mapping information, in support of mapping of logical addresses for respective pages to corresponding physical addresses for those pages via respective hashes or other types of content-based signatures, as described in further detail elsewhere herein.

The control modules 208C may further comprise additional components such as respective messaging interfaces that are utilized by the control modules 208C to process routing-to-control messages received from the routing modules 208R, and to generate control-to-routing messages for transmission to the routing modules 208R. Such messaging interfaces can also be configured to process instructions and other messages received from the management module 208M and to generate messages for transmission to the management module 208M.

The data modules 208D comprise respective control interfaces. These control interfaces support communication between the data modules 208D and the control modules 208C. Also included in the data modules are respective SSD interfaces. These SSD interfaces support communications with corresponding ones of the storage devices 206 of the CAS system 205.

The above-described processing module arrangements are presented by way of example only, and can be varied in other embodiments.

In some embodiments, a given data path of the CAS system 205 comprises a particular one of the routing modules 208R, a particular one of the control modules 208C and a particular one of the data modules 208D, each configured to handle different stages of the data path. For example, a given IO request can comprise a read request or a write request received in the particular control module from the particular routing module. The particular control module processes the received IO request to determine the particular data module that has access to the one or more data pages targeted by that IO request.

Communication links may be established between the various processing modules of the storage controller 208 using well-known communication protocols such as TCP/IP and RDMA. For example, respective sets of IP links used in data transfer and corresponding messaging could be associated with respective different ones of the routing modules 208R.

In some embodiments, at least portions of the functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in the CAS system are distributed over at least the control modules 208C and data modules 208D of storage controller 208. Numerous other arrangements are possible. For example, portions of the functionality can be implemented in the one or more management modules 208, or using other types and arrangements of modules within or outside of the storage controller 208.

As indicated previously, the storage devices 206 are configured to store metadata pages 220 and user data pages 222, and one or more of the journals in the set of distributed journals 214, and may also store additional information not explicitly shown such as, for example, one or more system checkpoints and/or snapshots of storage volumes. The metadata pages 220 and the user data pages 222 in some embodiments are illustratively stored in respective designated metadata and user data areas of the storage devices 206. Accordingly, metadata pages 220 and user data pages 222 may be viewed as corresponding to respective designated metadata and user data areas of the storage devices 206.

As noted above, a given “page” as the term is broadly used herein should not be viewed as being limited to any particular range of fixed sizes. In some embodiments, a page size of 8 KB is used, but this is by way of example only and can be varied in other embodiments. For example, page sizes of 4 KB, 16 KB or other values can be used. Accordingly, illustrative embodiments can utilize any of a wide variety of alternative paging arrangements for organizing the metadata pages 220 and the user data pages 222.

The user data pages 222 are part of a plurality of logical storage volumes configured to store files, blocks, objects or other arrangements of data, each also generally referred to herein as a “data item,” on behalf of users of the CAS system 205. Each such logical storage volume may comprise particular ones of the above-noted user data pages 222 of the user data area. The user data stored in the user data pages 222 can include any type of user data that may be utilized in the system 100. The term “user data” herein is therefore also intended to be broadly construed.

A given storage volume for which content-based signatures are generated, illustratively by signature generators implemented in respective ones of the control modules 208C and/or elsewhere in the storage nodes 202, can comprise a set of one or more LUNs, each including multiple ones of the user data pages 222 stored in storage devices 206.

The CAS system 205 in the embodiment of FIG. 2 is configured to generate hash metadata providing a mapping between content-based digests of respective ones of the user data pages 222 and corresponding physical locations of those pages in the user data area. Content-based digests generated using hash functions are also referred to herein as “hash digests.” Such hash digests or other types of content-based digests are examples of what are more generally referred to herein as “content-based signatures” of the respective user data pages 222. The hash metadata generated by the CAS system 205 is illustratively stored as metadata pages 220 in the metadata area. The generation and storage of the hash metadata is assumed to be performed under the control of the storage controller 208.

Each of the metadata pages 220 characterizes a plurality of the user data pages 222. For example, in a given set of user data pages representing a portion of the user data pages 222, each of the user data pages is characterized by a volume identifier, an offset and a content-based signature. The content-based signature is generated as a hash function of content of the corresponding user data page. Illustrative hash functions that may be used to generate the content-based signature include the above-noted SHA1 secure hashing algorithm, or other secure hashing algorithms known to those skilled in the art, including SHA2, SHA256 and many others. The content-based signature is utilized to determine the location of the corresponding user data page within the user data area of the storage devices 206.

Each of the metadata pages 220 in the present embodiment is assumed to have a signature that is not content-based. For example, the metadata page signatures may be generated using hash functions or other signature generation algorithms that do not utilize content of the metadata pages as input to the signature generation algorithm. Also, each of the metadata pages is assumed to characterize a different set of the user data pages.

A given set of metadata pages representing a portion of the metadata pages 220 in an illustrative embodiment comprises metadata pages having respective signatures. Each such metadata page characterizes a different set of user data pages. For example, the characterizing information in each metadata page can include the volume identifiers, offsets and content-based signatures for each of the user data pages that are characterized by that metadata page. It is to be appreciated, however, that the user data and metadata page configurations described above are examples only, and numerous alternative user data and metadata page configurations can be used in other embodiments.

Ownership of a user data logical address space within the CAS system 205 is illustratively distributed among the control modules 208C.

The functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in the CAS system 205 in this embodiment is assumed to be distributed across multiple distributed processing modules, including at least a subset of the processing modules 208C, 208D, 208R and 208M of the storage controller 208.

For example, the management module 208M of the storage controller 208 may include a snapshot pair creation logic instance that engages corresponding snapshot pair creation logic instances in all of the control modules 208C in order to support dynamic modification of IO shaping mechanisms in the CAS system 205.

In some embodiments, each of the user data pages 222 has a fixed size such as, for example, 8 KB, and its content-based signature is a 20-byte signature generated using the SHA1 secure hashing algorithm. Also, each page has a volume identifier and an offset, and so is characterized by <lun_id, offset, signature>.

The content-based signature in the present example comprises a content-based digest of the corresponding data page. Such a content-based digest is more particularly referred to as a “hash digest” of the corresponding data page, as the content-based signature is illustratively generated by applying a hash function such as the SHA1 secure hashing algorithm to the content of that data page. The full hash digest of a given data page is given by the above-noted 20-byte signature. The hash digest may be represented by a corresponding “hash handle,” which in some cases may comprise a particular portion of the hash digest. The hash handle illustratively maps on a one-to-one basis to the corresponding full hash digest within a designated cluster boundary or other specified storage resource boundary of a given storage system. In arrangements of this type, the hash handle provides a lightweight mechanism for uniquely identifying the corresponding full hash digest and its associated data page within the specified storage resource boundary. The hash digest and hash handle are both considered examples of “content-based signatures” as that term is broadly used herein.

Examples of techniques for generating and processing hash handles for respective hash digests of respective data pages are disclosed in U.S. Pat. No. 9,208,162, entitled “Generating a Short Hash Handle,” and U.S. Pat. No. 9,286,003, entitled “Method and Apparatus for Creating a Short Hash Handle Highly Correlated with a Globally-Unique Hash Signature,” both of which are incorporated by reference herein.

The storage controller 208 in this example is configured to group consecutive pages into page groups, to arrange the page groups into slices, and to assign the slices to different ones of the control modules 208C. For example, if there are 1024 slices distributed evenly across the control modules 208C, and there are a total of 16 control modules in a given implementation, each of the control modules “owns” 1024/16=64 slices. In such arrangements, different ones of the slices are assigned to different ones of the control modules 208C such that control of the slices within the storage controller 208 of the CAS system 205 is substantially evenly distributed over the control modules 208C of the storage controller 208.

The data modules 208D allow a user to locate a given user data page based on its signature. Each metadata page also has a size of 8 KB and includes multiple instances of the <lun_id, offset, signature> for respective ones of a plurality of the user data pages 222. Such metadata pages 220 are illustratively generated by the control modules 208C but are accessed using the data modules 208D based on a metadata page signature.

The metadata page signature in this embodiment is a 20-byte signature but is not based on the content of the metadata page. Instead, the metadata page signature is generated based on an 8-byte metadata page identifier that is a function of the volume identifier and offset information of that metadata page.

If a user wants to read a user data page having a particular volume identifier and offset, the corresponding metadata page identifier is first determined, then the metadata page signature is computed for the identified metadata page, and then the metadata page is read using the computed signature. In this embodiment, the metadata page signature is more particularly computed using a signature generation algorithm that generates the signature to include a hash of the 8-byte metadata page identifier, one or more ASCII codes for particular predetermined characters, as well as possible additional fields. The last bit of the metadata page signature may always be set to a particular logic value so as to distinguish it from the user data page signature in which the last bit may always be set to the opposite logic value.

The metadata page signature is used to retrieve the metadata page via the data module. This metadata page will include the <lun_id, offset, signature> for the user data page if the user page exists. The signature of the user data page is then used to retrieve that user data page, also via the data module.

Write requests processed in the CAS system 205 each illustratively comprise one or more IO operations directing that at least one data item of the CAS system 205 be written to in a particular manner. A given write request is illustratively received in the CAS system 205 from one of the host devices 101 over network 104. In some embodiments, a write request is received in the storage controller 208 of the CAS system 205, and directed from one processing module to another processing module of the storage controller 208. For example, a received write request may be directed from a routing module 208R of the storage controller 208 to a particular control module 208C of the storage controller 208. Other arrangements for receiving and processing write requests from one or more of the host devices 101 can be used.

The term “write request” as used herein is intended to be broadly construed, so as to encompass one or more IO operations directing that at least one data item of a storage system be written to in a particular manner. A given write request is illustratively received in a storage system from a host device.

In some embodiments, the control modules 208C, data modules 208D and routing modules 208R of the storage nodes 202 communicate with one another over a high-speed internal network such as an InfiniBand network. The control modules 208C, data modules 208D and routing modules 208R coordinate with one another to accomplish various IO processing tasks, as described elsewhere herein.

The write requests from the host devices identify particular data pages to be written in the CAS system 205 by their corresponding logical addresses each illustratively comprising a volume identifier and an offset.

As noted above, a given one of the content-based signatures illustratively comprises a hash digest of the corresponding data page, with the hash digest being generated by applying a hash function to the content of that data page. The hash digest may be uniquely represented within a given storage resource boundary by a corresponding hash handle.

The CAS system 205 illustratively utilizes a two-level mapping process to map logical block addresses to physical block addresses. In some embodiments, the first level of mapping uses an A2H table and the second level of mapping uses an HMD table, with the A2H and HMD tables corresponding to respective logical and physical layers of the content-based signature mapping within the CAS system 205. The HMD table or a given portion thereof in some embodiments disclosed herein is more particularly referred to as an H2D table or H2P table, although it is to be understood that these and other mapping tables or other metadata structures referred to herein can be varied in other embodiments.

The first level of mapping using the A2H table associates logical addresses of respective data pages with respective content-based signatures of those data pages. This is also referred to as logical layer mapping.

The second level of mapping using the HMD table associates respective ones of the content-based signatures with respective physical storage locations in one or more of the storage devices 206. This is also referred to as physical layer mapping.

Examples of these and other metadata structures utilized in illustrative embodiments were described elsewhere herein. These particular examples illustratively include respective A2H, H2D, HMD and PLB tables. In some embodiments, the A2H and H2D tables are utilized primarily by the control modules 208C, while the HMD and PLB tables are utilized primarily by the data modules 208D.

For a given write request, hash metadata comprising at least a subset of the above-noted tables is updated in conjunction with the processing of that write request.

The A2H, H2D, HMD and PLB tables described above are examples of what are more generally referred to herein as “mapping tables” of respective distinct types. Other types and arrangements of mapping tables or other content-based signature mapping information may be used in other embodiments.

Such mapping tables are still more generally referred to herein as “metadata structures” of the CAS system 205. It should be noted that additional or alternative metadata structures can be used in other embodiments. References herein to particular tables of particular types, such as A2H, H2D, HMD and PLB tables, and their respective configurations, should be considered non-limiting and are presented by way of illustrative example only. Such metadata structures can be implemented in numerous alternative configurations with different arrangements of fields and entries in other embodiments.

The logical block addresses or LBAs of a logical layer of the CAS system 205 correspond to respective physical blocks of a physical layer of the CAS system 205. The user data pages of the logical layer are organized by LBA and have reference via respective content-based signatures to particular physical blocks of the physical layer.

Each of the physical blocks has an associated reference count that is maintained within the CAS system 205. The reference count for a given physical block indicates the number of logical blocks that point to that same physical block.

In releasing logical address space in the storage system, a dereferencing operation is generally executed for each of the LBAs being released. More particularly, the reference count of the corresponding physical block is decremented. A reference count of zero indicates that there are no longer any logical blocks that reference the corresponding physical block, and so that physical block can be released.

It should also be understood that the particular arrangement of storage controller processing modules 208C, 208D, 208R and 208M as shown in the FIG. 2 embodiment is presented by way of example only. Numerous alternative arrangements of processing modules of a distributed storage controller may be used to implement dynamic modification of IO shaping mechanisms in a distributed CAS system or other type of distributed storage system in other embodiments.

Additional examples of content addressable storage functionality that may be implemented in some embodiments by control modules 208C, data modules 208D, routing modules 208R and management module(s) 208M of storage controller 208 can be found in U.S. Pat. No. 9,104,326, entitled “Scalable Block Data Storage Using Content Addressing,” which is incorporated by reference herein. Alternative arrangements of these and other storage node processing modules of a distributed storage controller in a distributed CAS system or other type of distributed storage system can be used in other embodiments.

As indicated above, the CAS system 205 illustratively comprises storage nodes 202 interconnected in a mesh network, with each such storage node comprising a set of processing modules configured to communicate with corresponding sets of processing modules on other ones of the storage nodes. A given such set of processing modules comprises at least a routing module, a control module and a data module, with the sets of processing modules of the storage nodes 202 of the CAS system 205 collectively comprising at least a portion of the storage controller 208 of the CAS system 205.

The storage nodes 202 and their respective sets of processing modules are managed by a system manager, illustratively implemented as a management module 208M within the set of processing modules on at least one of the storage nodes 202. Each of the storage nodes 202 illustratively comprises a CPU or other type of processor, a memory, a network interface card (NIC) or other type of network interface, and a subset of the storage devices 206, possibly arranged as part of a DAE of the storage node. These and other references to “disks” herein are intended to refer generally to storage devices, including SSDs, and should therefore not be viewed as limited in any way to spinning magnetic media.

An example of the operation of the CAS system 205 in processing IO operations will now be described with reference to FIG. 3, which shows the relationship between routing, control and data modules of one possible distributed implementation of CAS system 205 in an illustrative embodiment. More particularly, FIG. 3 illustrates a portion 300 of the CAS system 205, showing a routing module 208R-x, a control module 208C-y and a data module 208D-z in a distributed implementation of the storage controller 208. The routing module 208R-x, the control module 208C-y and the data module 208D-z are also denoted in this embodiment as an R-module, a C-module and a D-module, respectively.

These modules are respective processing modules of the storage controller 208, and are potentially located on different ones of the storage nodes 202 of the CAS system 205. For example, each of the storage nodes 202 of the CAS system 205 illustratively comprises at least one R-module, at least one C-module and at least one D-module, although many other storage node configurations are possible. In the present embodiment, the routing module 208R-x, the control module 208C-y and the data module 208D-z are assumed to be on respective different storage nodes x, y and z of the CAS system 205. The storage nodes x, y and z represent respective particular ones of the storage nodes 202. The storage node z that implements the D-module 208D-z comprises a subset of the storage devices 206 of the CAS system 205, with the subset of storage devices 206 on storage node z being denoted as storage devices 206-z. Each of the other storage nodes 202 of the CAS system 205 similarly has a different subset of the storage devices 206 associated therewith.

It is assumed in this example that the CAS system 205 manages data using a fixed-size page granularity (e.g., 4 KB, 8 KB or 16 KB), also referred to herein as the native page size of the CAS system 205. A unique hash digest is computed for each of the data pages by a content-based signature generator, illustratively using SHA1 or another secure hashing algorithm of the type described elsewhere herein.

In the CAS system 205, routing modules 208R such as R-module 208R-x illustratively include a storage command parser as shown, such as a SCSI command parser, although other command parsers for other storage protocols can be used in other embodiments. The routing modules 208R receive IO requests from one or more of the host devices 101, parse the corresponding storage commands and route them to the appropriate control modules 208C, which may be located on different storage nodes 202, illustratively using an address-to-control (“A2C”) table. The A2C table maps different portions of a logical address space of the CAS system 205 across different ones of the control modules 208C. A given IO request can be sent by the corresponding one of the host devices 101 to any of the routing modules 208R of the CAS system 205.

The control modules 208C such as control module 208C-y receive the IO requests from the routing modules 208R, and use mapping tables such as the above-described A2H and H2D tables to identify the appropriate data modules 208D that store the corresponding data pages in the distributed CAS system 205. This illustratively includes performing a logical address to hash mapping as shown in the figure.

In processing read requests, the C-module 208C-y retrieves from the A2H table the hash digests of the corresponding requested pages, and sends read requests to the appropriate data modules 208D based on the H2D table.

In processing write requests, the C-module 208C-y illustratively computes the hash digests of the data pages based on the write data, sends write requests to the corresponding data modules 208D as determined from the H2D table, and updates the A2H table.

The data modules 208D such as D-module 208D-z are responsible for the physical storage of the data pages, and use mapping tables such as the above-described HMD and PLB tables and/or other types of H2P tables to determine the physical location of a given data page in the subset of storage devices 206 associated with that data module, using a hash digest, hash handle or other content-based signature supplied by a control module. This illustratively includes performing a hash to physical location mapping as shown in the figure. Such a hash to physical location mapping can utilize an H2P table of the type described elsewhere herein, illustratively comprising at least portions of the above-noted HMD and PLB tables. The data modules 208D in some embodiments additionally store a copy or “mirror” of such metadata in a memory of the respective corresponding storage nodes 202, in order to optimize performance by reducing accesses to the associated storage devices 206 during system operation.

A given one of the host devices 101 illustratively sends an IO request to a particular one of the routing modules 208R, possibly using random selection or another type of algorithm such as round robin to select a particular routing module for a particular IO request. Such selection can be implemented as part of a path selection algorithm performed by a multi-path input-output (MPIO) driver of the host device, in order to select a particular path comprising an initiator-target pair for delivery of the IO request to the CAS system 205. The initiator illustratively comprises a particular host bus adaptor (HBA) of the given host device, and the target illustratively comprises a particular port of the CAS system 205.

The processing of write requests in the CAS system 205 makes use of the distributed write cache 212 and the distributed write cache journal of the set of distributed journals 214, as will now be described in more detail. For example, such processing can include first, second and third stages in the processing of a given write request, with the stages also being referred to herein as Stage 1, Stage 2 and Stage 3, respectively.

The write cache 212 illustratively comprises a volatile memory of the CAS system 205 for temporarily storing data pages associated with write requests received from the host devices 101. The write cache 212 is backed up by the write cache journal that stores persisted copies of the write data. The write data of the write cache 212 is later “destaged” to a persistent data storage location in the storage devices 106. For write requests received by the CAS system 205, address locks are placed on corresponding data pages, illustratively by associated ones of the control modules 208C, until the destaging of those data pages from the write cache 212 is completed.

A given write request targeting a particular data page in the CAS system 205 is illustratively processed by one of the control modules 208C and one of the data modules 208D in accordance with the following multi-stage process, the primary steps of which correspond generally to Stage 1, Stage 2 and Stage 3 of the processing of a given write request:

1. Front-end write request processing by control module. This is a synchronous stage handled by a particular control module, initiated upon receipt of the write request from one of the routing modules 208R. The data page is stored in an entry of the write cache 212 and persisted in the write cache journal, and the write request is then acknowledged back to the host device. The A2H table entry corresponding to the logical address of the data page being written is updated to point to the entry of the data page in the write cache 212.

2. Write cache destaging to data module. This is an asynchronous stage initiated by the control module to destage the data page to its appropriate destination data module. This stage more particularly implements an example write cache destaging process that includes the following steps:

-   -   (a) Control module acquires address lock for data page;     -   (b) Control module sends destage request to data module;     -   (c) Data module loads associated metadata into RAM if not         already loaded;     -   (d) Data module writes data page and associated metadata and         replies to control module with the hash digest of the data page;     -   (e) Control module updates its A2H table to include the hash         digest of the data page; and     -   (f) Control module releases address lock for data page.

3. Background destaging from data module to storage device. This is an asynchronous stage initiated by the data module that persists the data page to a storage device associated with the data module. The data module stores the data page in the storage device and then updates its H2P table to point to the corresponding physical location.

The second stage as described above may be viewed an example of what is more generally referred to herein as a “write cache destaging process.” That term as broadly used herein can encompass other types of additional or alternative processing operations, and may include at least portions of one or more other stages.

At the completion of the second stage, the data associated with the write request is still not written to the storage devices 106. The data is stored in the volatile memories of the write cache 212 and the data module 208D, and is persisted in the write cache journal for backup and recovery purposes.

The third stage of the write request handling process is illustratively performed asynchronously with the first and second stages, but in other embodiments may be performed synchronously with one or both of the other stages.

At the completion of the third stage, the data is persisted in the storage devices 106 so the corresponding write cache entry and its write cache journal entry are no longer needed, and can be deleted.

Other types and arrangements of write request processing stages can be used in other embodiments.

The CAS system 205 is configured to provide dynamic modification of IO shaping mechanisms of the storage nodes 202 using operations similar to those previously described in conjunction with FIG. 1.

These and other operations associated with dynamic modification of IO shaping mechanisms in the CAS system 205 are illustratively performed at least in part by or under the control of the storage controller 208 and at least a subset of its processing modules 208C, 208D, 208R and 208M.

The particular features described above in conjunction with FIGS. 2 and 3 should not be construed as limiting in any way, and a wide variety of other distributed implementations of storage nodes 202 are possible.

The particular set of storage nodes 102 or 202 of the respective example distributed storage systems illustrated in FIGS. 1 and 2 is assumed to be implemented using at least one processing platform, with each such processing platform comprising one or more processing devices, and each such processing device comprising a processor coupled to a memory. Such processing devices can illustratively include particular arrangements of compute, storage and network resources.

The storage nodes may be implemented on respective distinct processing platforms, although numerous other arrangements are possible. At least portions of their associated host devices may be implemented on the same processing platforms as the storage nodes or on separate processing platforms.

The term “processing platform” as used herein is intended to be broadly construed so as to encompass, by way of illustration and without limitation, multiple sets of processing devices and associated storage systems that are configured to communicate over one or more networks. For example, distributed implementations of the system 100 are possible, in which certain components of the system reside in one data center in a first geographic location while other components of the system reside in one or more other data centers in one or more other geographic locations that are potentially remote from the first geographic location. Thus, it is possible in some implementations of the system 100 for the storage nodes 102 to reside in different data centers. Numerous other distributed implementations of the storage nodes 102 and their respective associated sets of host devices are possible. Similarly, various distributed implementations of CAS system 205 and its storage nodes 202 are possible.

Additional examples of processing platforms utilized to implement storage systems and possibly their associated host devices in illustrative embodiments will be described in more detail below in conjunction with FIGS. 5 and 6.

It is to be appreciated that these and other features of illustrative embodiments are presented by way of example only, and should not be construed as limiting in any way.

Accordingly, different numbers, types and arrangements of system components such as host devices 101, storage nodes 102 or 202, network 104, storage devices 106 or 206, storage controllers 108 or 208, local and remote interface logic 110 and IO shaping mechanisms 111 can be used in other embodiments.

It should be understood that the particular sets of modules and other components implemented in a distributed storage system as illustrated in FIGS. 1, 2 and 3 are presented by way of example only. In other embodiments, only subsets of these components, or additional or alternative sets of components, may be used, and such components may exhibit alternative functionality and configurations.

For example, in other embodiments, functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes can be implemented in one or more host devices, or partially in a host device and partially in a storage system. Accordingly, illustrative embodiments are not limited to arrangements in which all such functionality is implemented in a storage system or a host device, and therefore encompass various hybrid arrangements in which the functionality is distributed over one or more storage systems and one or more associated host devices, each comprising one or more processing devices.

The operation of the information processing system 100 will now be described in further detail with reference to the flow diagram of the illustrative embodiment of FIG. 4, which implements a process for dynamic modification of IO shaping mechanisms in a distributed storage system. This process may be viewed as an example algorithm implemented at least in part by the storage controllers 108 of the respective storage nodes 102 of the distributed storage system of FIG. 1. For example, such an algorithm is illustratively carried out by one or more instances of local and remote interface logic 110 and IO shaping mechanisms 111 in respective ones of the storage controllers 108. Such an algorithm can also be implemented by the distributed storage controller 208 and its processing modules 208C, 208D, 208R and 208M distributed over the storage nodes 202 of CAS system 205 of FIG. 2. These and other algorithms disclosed herein are more generally applicable to a wide variety of other distributed storage systems each comprising two or more storage nodes.

Referring now to FIG. 4, the process as illustrated includes steps 400 through 410, and implements dynamic modification of IO shaping mechanism in multiple storage nodes of a distributed storage system. It is assumed that the steps of the process are performed by multiple storage nodes of the distributed storage system, which may illustratively include the storage nodes 102-1 and 102-2 of FIG. 1, and possibly additional storage nodes 102-3 through 102-N, or at least a subset of the storage nodes 202 of FIG. 2. The other storage nodes 102 or 202 are assumed to implement similar functionality.

In step 400, a failure event impacting at least one storage node of the distributed storage system is detected. As indicated previously, a “failure event” as that term is broadly used herein can comprise, for example, a failure of one or more processes executing on the storage node, a failure of one or more local persistent storage devices of the storage node, a failure of the storage node itself, or another type of HA event that adversely impacts the desired HA of the distributed storage system.

It is assumed that the failure event is detected by a system manager of the distributed storage system, such as the management module 208M in the storage controller 208 of a given one of the storage nodes 202. A system manager may be similarly implemented in at least one of the storage nodes 102 of system 100.

In step 402, the system manager generates backpressure hints for multiple storage nodes of the distributed storage system. For example, a single backpressure hint can be replicated for each of the storage nodes, or different backpressure hints containing different types of information can be generated for different ones of the storage nodes.

In step 404, the system manager sends the backpressure hints to the multiple storage nodes, possibly using a dedicated channel to each such storage node. Other types of connections can be used in other embodiments.

In step 406, each storage node receiving a backpressure hint from the system manager modifies its IO shaping mechanism by switching from a normal mode to a mode providing enhanced responsiveness to any latency issues for individual IOs.

In step 408, a determination is made as to whether or not the failure event has been fully resolved. If the failure event has been fully resolved, the process moves to step 410, and otherwise returns to step 406 such that the IO shaping mechanisms continue to operate in their respective enhanced responsiveness modes. In some embodiments, the system manager determines when a failure event has been fully resolves, and notifies each of the storage nodes accordingly, although other arrangements are possible.

In step 410, which is reached after the detected failure event has been fully resolved, the storage nodes switch their IO shaping mechanisms back to the normal mode.

Similar operations may be performed on one or more additional storage nodes of the distributed storage system, responsive to failure events that impact those one or more additional storage nodes.

The steps are shown in sequential order for clarity and simplicity of illustration only, and certain steps can at least partially overlap with other steps.

The particular processing operations and other system functionality described in conjunction with the flow diagram of FIG. 4 are presented by way of illustrative example only, and should not be construed as limiting the scope of the disclosure in any way. Alternative embodiments can use other types of processing operations for dynamic modification of IO shaping mechanisms of multiple storage nodes in a distributed storage system. For example, as indicated above, the ordering of the process steps may be varied in other embodiments, or certain steps may be performed at least in part concurrently with one another rather than serially. Also, one or more of the process steps may be repeated periodically, or multiple instances of the processes can be performed in parallel with one another in order to implement a plurality of different dynamic modification processes for respective different distributed storage systems or portions thereof within a given information processing system.

Functionality such as that described in conjunction with the flow diagram of FIG. 4 can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device such as a computer or server. As will be described below, a memory or other storage device having executable program code of one or more software programs embodied therein is an example of what is more generally referred to herein as a “processor-readable storage medium.”

A distributed storage controller as disclosed herein is illustratively configured to implement functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes, such as an algorithm comprising a process of the type shown in FIG. 4. Such a distributed storage controller can comprise, for example, storage controllers 108-1 and 108-2 of FIG. 1 or distributed storage controller 208 in CAS system 205, as configured to perform the steps of the process of FIG. 4.

A distributed storage controller can be implemented as part of what is more generally referred to herein as a processing platform comprising one or more processing devices each comprising a processor coupled to a memory.

A given such processing device in some embodiments may correspond to one or more virtual machines or other types of virtualization infrastructure such as Docker containers or Linux containers (LXCs). Host devices, distributed storage controllers and other system components may be implemented at least in part using processing devices of such processing platforms. For example, respective distributed modules of a distributed storage controller can be implemented in respective containers running on respective ones of the processing devices of a processing platform.

Illustrative embodiments of a distributed storage system with functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes as disclosed herein can provide a number of significant advantages relative to conventional arrangements.

For example, illustrative embodiments provide an IO shaping mechanism enhancement to improve distributed storage system stability and provide a higher throughput during unexpected failures.

Using this IO shaping mechanism enhancement, the system will be able to quickly respond to HA events that have an instant and considerable impact on the system, by reducing parallelism before an increased systemic load overwhelms the system. Such embodiments therefore avoid further system degradation that might otherwise occur absent use of the disclosed IO shaping mechanism enhancement.

In some embodiments, individual IO shaping mechanisms of respective ones of multiple storage nodes in a distributed storage system are dynamically switched from a first mode using average latency over multiple IO operations to a second mode using latencies of individual IO operations, responsive to a detected failure event.

Such embodiments can advantageously prevent a failure event involving a particular one of the storage nodes from unduly interfering with efficient processing of host device IO operations by other ones of the storage nodes of the distributed storage system, thereby significantly improving the overall IO processing performance of the distributed storage system in the presence of a wide variety of different types of storage node failures.

For example, host device IO “timeouts” or other substantial IO processing delays that might otherwise arise on the other storage nodes when using conventional approaches are eliminated or substantially reduced in illustrative embodiments disclosed herein.

It is to be appreciated that the particular advantages described above and elsewhere herein are associated with particular illustrative embodiments and need not be present in other embodiments. Also, the particular types of information processing system features and functionality as illustrated in the drawings and described above are exemplary only, and numerous other arrangements may be used in other embodiments.

Illustrative embodiments of processing platforms utilized to implement host devices and distributed storage systems with functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes will now be described in greater detail with reference to FIGS. 5 and 6. Although described in the context of system 100, these platforms may also be used to implement at least portions of other information processing systems in other embodiments.

FIG. 5 shows an example processing platform comprising cloud infrastructure 500. The cloud infrastructure 500 comprises a combination of physical and virtual processing resources that may be utilized to implement at least a portion of the information processing system 100. The cloud infrastructure 500 comprises multiple virtual machines (VMs) and/or container sets 502-1, 502-2, . . . 502-L implemented using virtualization infrastructure 504. The virtualization infrastructure 504 runs on physical infrastructure 505, and illustratively comprises one or more hypervisors and/or operating system level virtualization infrastructure. The operating system level virtualization infrastructure illustratively comprises kernel control groups of a Linux operating system or other type of operating system.

The cloud infrastructure 500 further comprises sets of applications 510-1, 510-2, . . . 510-L running on respective ones of the VMs/container sets 502-1, 502-2, . . . 502-L under the control of the virtualization infrastructure 504. The VMs/container sets 502 may comprise respective VMs, respective sets of one or more containers, or respective sets of one or more containers running in VMs.

In some implementations of the FIG. 5 embodiment, the VMs/container sets 502 comprise respective VMs implemented using virtualization infrastructure 504 that comprises at least one hypervisor. Such implementations can provide functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in a distributed storage system of the type described above using one or more processes running on a given one of the VMs. For example, each of the VMs can implement logic instances and/or other components for implementing functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in the CAS system 205.

A hypervisor platform may be used to implement a hypervisor within the virtualization infrastructure 504. Such a hypervisor platform may comprise an associated virtual infrastructure management system. The underlying physical machines may comprise one or more distributed processing platforms that include one or more storage systems.

In other implementations of the FIG. 5 embodiment, the VMs/container sets 502 comprise respective containers implemented using virtualization infrastructure 504 that provides operating system level virtualization functionality, such as support for Docker containers running on bare metal hosts, or Docker containers running on VMs. The containers are illustratively implemented using respective kernel control groups of the operating system. Such implementations can also provide functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in a distributed storage system of the type described above. For example, a container host device supporting multiple containers of one or more container sets can implement logic instances and/or other components for implementing functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes in the CAS system 205.

As is apparent from the above, one or more of the processing modules or other components of system 100 may each run on a computer, server, storage device or other processing platform element. A given such element may be viewed as an example of what is more generally referred to herein as a “processing device.” The cloud infrastructure 500 shown in FIG. 5 may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform 600 shown in FIG. 6.

The processing platform 600 in this embodiment comprises a portion of system 100 and includes a plurality of processing devices, denoted 602-1, 602-2, 602-3, . . . 602-K, which communicate with one another over a network 604.

The network 604 may comprise any type of network, including by way of example a global computer network such as the Internet, a WAN, a LAN, a satellite network, a telephone or cable network, a cellular network, a wireless network such as a WiFi or WiMAX network, or various portions or combinations of these and other types of networks.

The processing device 602-1 in the processing platform 600 comprises a processor 610 coupled to a memory 612.

The processor 610 may comprise a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), graphics processing unit (GPU) or other type of processing circuitry, as well as portions or combinations of such circuitry elements.

The memory 612 may comprise random access memory (RAM), read-only memory (ROM), flash memory or other types of memory, in any combination. The memory 612 and other memories disclosed herein should be viewed as illustrative examples of what are more generally referred to as “processor-readable storage media” storing executable program code of one or more software programs.

Articles of manufacture comprising such processor-readable storage media are considered illustrative embodiments. A given such article of manufacture may comprise, for example, a storage array, a storage disk or an integrated circuit containing RAM, ROM, flash memory or other electronic memory, or any of a wide variety of other types of computer program products. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals. Numerous other types of computer program products comprising processor-readable storage media can be used.

Also included in the processing device 602-1 is network interface circuitry 614, which is used to interface the processing device with the network 604 and other system components, and may comprise conventional transceivers.

The other processing devices 602 of the processing platform 600 are assumed to be configured in a manner similar to that shown for processing device 602-1 in the figure.

Again, the particular processing platform 600 shown in the figure is presented by way of example only, and system 100 may include additional or alternative processing platforms, as well as numerous distinct processing platforms in any combination, with each such platform comprising one or more computers, servers, storage devices or other processing devices.

For example, other processing platforms used to implement illustrative embodiments can comprise converged infrastructure such as VxRail™, VxRack™, VxRack™ FLEX, VxBlock™ or Vblock® converged infrastructure from Dell EMC.

It should therefore be understood that in other embodiments different arrangements of additional or alternative elements may be used. At least a subset of these elements may be collectively implemented on a common processing platform, or each such element may be implemented on a separate processing platform.

As indicated previously, components of an information processing system as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device. For example, at least portions of the functionality for dynamic modification of IO shaping mechanisms of multiple storage nodes provided by one or more components of a storage system as disclosed herein are illustratively implemented in the form of software running on one or more processing devices.

It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. For example, the disclosed techniques are applicable to a wide variety of other types of information processing systems, host devices, storage systems, storage nodes, storage devices, storage controllers, local and remote interfaces, IO shaping mechanisms and other components. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art. 

What is claimed is:
 1. An apparatus comprising: at least one processing device comprising a processor coupled to a memory; said at least one processing device being configured: to detect a failure event impacting at least a first storage node of a plurality of storage nodes of a distributed storage system; and responsive to the detected failure event, to modify an input-output shaping mechanism in each of the storage nodes in order to at least temporarily reduce a total number of input-output operations that are concurrently processed in the distributed storage system; wherein modifying an input-output shaping mechanism in each of the storage nodes comprises transitioning the input-output shaping mechanism in each of the storage nodes from a first operating mode to a second operating mode that is different than the first operating mode; and wherein the second operating mode of the input-output shaping mechanism has a relatively faster responsiveness to changes in input-output operation latency as compared to the first operating mode of the input-output shaping mechanism.
 2. The apparatus of claim 1 wherein the detected failure event comprises at least one of: (i) a failure of at least one process executing on the first storage node, (ii) a failure of one or more storage devices of the first storage node, and (iii) a failure of the first storage node itself.
 3. The apparatus of claim 1 wherein the input-output shaping mechanism in the first operating mode controls processing of input-output operations in order to satisfy one or more constraints relating to average latency over a plurality of input-output operations.
 4. The apparatus of claim 1 wherein the input-output shaping mechanism in the second operating mode controls processing of input-output operations in order to satisfy one or more constraints relating to latency of individual input-output operations.
 5. The apparatus of claim 1 wherein the input-output shaping mechanism in a given one of the storage nodes is configured to measure input-output operation latency and to adjust a number of parallel input-output operations processed by the given storage node responsive to the measurement of input-output operation latency.
 6. The apparatus of claim 1 wherein modifying an input-output shaping mechanism in each of the storage nodes further comprises: identifying a type of the detected failure event; and providing a particular one of a plurality of available modifications of the input-output shaping mechanism in a given one of the storage nodes based at least in part on the identified type of the detected failure event.
 7. The apparatus of claim 6 wherein different ones of the plurality of available modifications of the input-output shaping mechanism provide different reductions in number of parallel input-output operations processed by the given storage node.
 8. The apparatus of claim 1 wherein each of at least a subset of the storage nodes of the distributed storage system comprises a set of processing modules configured to communicate with corresponding sets of processing modules on other ones of the storage nodes, a given such set of processing modules comprising at least a routing module, a control module and a data module, the sets of processing modules of the storage nodes of the distributed storage system collectively comprising at least a portion of a distributed storage controller of the distributed storage system.
 9. The apparatus of claim 1 wherein modifying operation of an input-output shaping mechanism in each of the storage nodes comprises: generating a backpressure hint; and sending the backpressure hint to each of the storage nodes; wherein the input-output shaping mechanism is modified in each of the storage nodes responsive to receipt of the backpressure hint.
 10. The apparatus of claim 9 wherein generating the backpressure hint and sending the backpressure hint to each of the storage nodes are performed by a system manager of the distributed storage system.
 11. The apparatus of claim 9 wherein sending the backpressure hint to each of the storage nodes comprises sending the backpressure hint from a system manager of the distributed storage system over a dedicated channel to each of the storage nodes.
 12. The apparatus of claim 9 wherein a given one of the storage nodes in response to receipt of the backpressure hint modifies its input-output shaping mechanism to effectively reduce a number of parallel input-output operations processed by the given storage node.
 13. A method comprising: detecting a failure event impacting at least a first storage node of a plurality of storage nodes of a distributed storage system; and responsive to the detected failure event, modifying an input-output shaping mechanism in each of the storage nodes in order to at least temporarily reduce a total number of input-output operations that are concurrently processed in the distributed storage system; wherein modifying an input-output shaping mechanism in each of the storage nodes comprises transitioning the input-output shaping mechanism in each of the storage nodes from a first operating mode to a second operating mode that is different than the first operating mode; wherein the second operating mode of the input-output shaping mechanism has a relatively faster responsiveness to changes in input-output operation latency as compared to the first operating mode of the input-output shaping mechanism; and wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
 14. The method of claim 13 wherein modifying operation of an input-output shaping mechanism in each of the storage nodes comprises: generating a backpressure hint; and sending the backpressure hint to each of the storage nodes; wherein the input-output shaping mechanism is modified in each of the storage nodes responsive to receipt of the backpressure hint; and wherein a given one of the storage nodes in response to receipt of the backpressure hint modifies its input-output shaping mechanism to effectively reduce a number of parallel input-output operations processed by the given storage node.
 15. The method of claim 14 wherein generating the backpressure hint and sending the backpressure hint to each of the storage nodes are performed by a system manager of the distributed storage system.
 16. The method of claim 14 wherein sending the backpressure hint to each of the storage nodes comprises sending the backpressure hint from a system manager of the distributed storage system over a dedicated channel to each of the storage nodes.
 17. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes said at least one processing device: to detect a failure event impacting at least a first storage node of a plurality of storage nodes of a distributed storage system; and responsive to the detected failure event, to modify an input-output shaping mechanism in each of the storage nodes in order to at least temporarily reduce a total number of input-output operations that are concurrently processed in the distributed storage system; wherein modifying an input-output shaping mechanism in each of the storage nodes comprises transitioning the input-output shaping mechanism in each of the storage nodes from a first operating mode to a second operating mode that is different than the first operating mode; and wherein the second operating mode of the input-output shaping mechanism has a relatively faster responsiveness to changes in input-output operation latency as compared to the first operating mode of the input-output shaping mechanism.
 18. The computer program product of claim 17 wherein modifying operation of an input-output shaping mechanism in each of the storage nodes comprises: generating a backpressure hint; and sending the backpressure hint to each of the storage nodes; wherein the input-output shaping mechanism is modified in each of the storage nodes responsive to receipt of the backpressure hint; and wherein a given one of the storage nodes in response to receipt of the backpressure hint modifies its input-output shaping mechanism to effectively reduce a number of parallel input-output operations processed by the given storage node.
 19. The computer program product of claim 18 wherein generating the backpressure hint and sending the backpressure hint to each of the storage nodes are performed by a system manager of the distributed storage system.
 20. The computer program product of claim 18 wherein sending the backpressure hint to each of the storage nodes comprises sending the backpressure hint from a system manager of the distributed storage system over a dedicated channel to each of the storage nodes. 